Nature hates straight lines

Guest Post by Willis Eschenbach

Yeah, I know Nature doesn’t have human emotions, give me a break. I’m aware it is unscientific and dare I call it atavistic and perhaps even socially unseemly to say Nature “hates” straight lines, but hey, it’s a headline, cut me some poetic slack.

My point is, everyone is aware that nature doesn’t deal in straight lines. Natural things move in fits and starts along complex paths, not straight from point to point. Phenomena have thresholds and edges, not slow linear changes at the perimeter. Tree branches and coastlines are jagged and bent. Things move in arcs and circles, relationships are complex and cyclical. Very little in nature is linear, particularly in complex systems.

Forcing is generally taken to mean downward radiation measured at the TOA (top of atmosphere). The IPCC says that when TOA forcing changes, the surface temperature changes linearly with that TOA forcing change. If there is twice the forcing change (twice the change in solar radiation, for example), the IPCC says we’ll see twice the temperature change. The proportionality constant (not a variable but a constant) that the IPCC says linearly relates temperature and TOA forcing is called the “climate sensitivity”.

Figure 1. Photo of impending change in climate sensitivity.

Today I stumbled across the IPCC justification of this linearity assumption. This is the basis of their claim of the existence of a constant called “climate sensitivity”. I quote it below.

I’ve removed the references and broken it into paragraphs it for easy reading. The references are in the original cited above. I reproduce all of the text on the web page. This is their entire justification for the linearity assumption. Having solved linearity in a few sentences, they then proceed to other matters. Here is their entire scientific justification for the assumption of linearity between forcing and temperature change (emphasis mine):

Linearity of the Forcing-Response Relationship

Reporting findings from several studies, the TAR [IPCC Third Assessment Report] concluded that responses to individual RFs [Radiative Forcings] could be linearly added to gauge the global mean response, but not necessarily the regional response.

Since then, studies with several equilibrium and/or transient integrations of several different GCMs [Global Climate Models] have found no evidence of any nonlinearity for changes in greenhouse gases and sulphate aerosol. Two of these studies also examined realistic changes in many other forcing agents without finding evidence of a nonlinear response.

In all four studies, even the regional changes typically added linearly. However, Meehl et al observed that neither precipitation changes nor all regional temperature changes were linearly additive. This linear relationship also breaks down for global mean temperatures when aerosol-cloud interactions beyond the cloud albedo RF are included in GCMs. Studies that include these effects modify clouds in their models, producing an additional radiative imbalance.

Rotstayn and Penner (2001) found that if these aerosol-cloud effects are accounted for as additional forcing terms, the inference of linearity can be restored. Studies also find nonlinearities for large negative RFs, where static stability changes in the upper troposphere affect the climate feedback (e.g., Hansen et al., 2005).

For the magnitude and range of realistic RFs discussed in this chapter, and excluding cloud-aerosol interaction effects, there is high confidence in a linear relationship between global mean RF [radiative forcing] and global mean surface temperature response.

Now, what strikes you as odd about that explanation of the scientific basis for their claim of linearity?

Before I discuss the oddity of that IPCC explanation, a short recap regarding climate sensitivity. I have held elsewhere that climate sensitivity changes with temperature. I will repeat the example I used to show how climate sensitivity goes down as temperature rises. This can be seen clearly in the tropics.

In the morning the tropical ocean and land is cool, and the skies are clear. As a result, the surface warms rapidly with increasing solar radiation. Climate sensitivity (which is the amount of temperature change for a given change in forcing) is high. High sensitivity, in other words, means that small changes in solar forcing make large changes in surface temperature.

By late morning, the surface has warmed significantly. As a result of the rising temperature, cumulus clouds start to form. They block some of the sun. After that, despite increasing solar forcing, the surface does not warm as fast as before. In other words, climate sensitivity is lower.

In the afternoon, with continued surface warming, thunderstorms start to form. These bring cool air and cool rain from aloft, and move warm air from the surface aloft. They cool the surface in those and a number of other ways. Since thunderstorms are generated in response to rising temperatures, further temperature increases are quickly countered by increasing numbers of thunderstorms. This brings climate sensitivity near to zero.

Finally, thunderstorms have a unique ability. They can drive the surface temperature underneath them below the temperature at which the thunderstorm formed. In this case, we have local areas of negative climate sensitivity – the solar forcing can be increasing while the surface is cooling.

As you can see, in the real world the temperature cannot be calculated as some mythical constant “climate sensitivity” times the forcing change. Sensitivity goes down as temperature goes up in the tropics, the area where the majority of solar energy enters our climate system.

So the IPCC claim of linearity, of the imagined slavish response of surface temperature to a given change in TOA forcing, goes against our daily experience.

Let me now return to the question I posed earlier. I asked above what struck you as odd about the IPCC explanation of their claim of linearity regarding forcing and temperature. It’s not the fact that they think it is linear and I disagree. That is not noteworthy.

Here’s what made me stand back and genuflect in awe of their claims. Perhaps I missed it, but I didn’t see a single word about real world observations in that entire (and most important) justification for one of their core positions.

I didn’t see anyone referenced who said something like ‘We measured solar radiation and downwelling longwave radiation and temperature at this location, and guess what? Temperatures changed linearly with the changes in radiation.’ I didn’t see anything at all like that, you know, actual scientific observations that support linearity.

Instead, their claim seems to rest on the studies showing that scientists looked at four different climate models, and in each and every one of the models the temperature change was linearly related to forcing changes. And in addition, another model found the same thing, so the issue is settled to a “high confidence” …

I gotta confess, that wasn’t the first time I’ve walked away from the IPCC Report shaking my head, but that one deserves some kind of prize or award for sheer audacity of their logic. Not a prize for the fact that they think the relationship is linear when Nature nature hates straight lines, that’s understandable, it’s the IPCC after all.

It is the logic of their argument that left me stammering.

Of course the model results are linear. The models are linear. They don’t contain non-linear mechanisms. And of course, if you look at the results of linear models, you will conclude with “high confidence” that there is a linear relationship between forcing and temperature. They looked into five of them, and case closed.

I mean, you really gotta admire these guys. They are so far into their models that they actually are using the linearity of the model results to justify the assumption of linearity embodied in those same models … breathtaking.

I mean, I approve of people pulling themselves up by their own bootstraps, but that was too twisted for me. The circularity of their logic made my neck ache. I kept looking over my shoulder to see if the other end of their syllogism was circling behind to strike me again. That’s why I genuflected in awe. I was overcome by the sheer beauty of using a circular argument to claim that Nature moves in straight lines … those guys are artists.

Meanwhile, back in the real world, almost no such linear relationships exist. Nature constantly runs at the edge of turbulence, with no linearity in sight. As my example above shows, the climate sensitivity changes with the temperature.

And even that change in tropical climate sensitivity with temperature is not linear. It has two distinct thresholds. One is at the temperature where the cumulus start to form. The other is at the slightly higher temperature where the thunderstorms start to form. At each of these thresholds there is an abrupt change in the climate sensitivity. It is nowhere near linear.

Like other natural flow systems, the climate is constantly restructuring to run “as fast as it can.” In other words, it runs at the edge of turbulence, “up against the stops” for any given combination of conditions. In the case of the tropics, the “stops” that prevents overheating is the rapid proliferation of thunderstorms. These form rapidly in response to only a slight temperature rise above the temperature threshold where the first thunderstorm forms. Above that threshold, most of any increase in the incoming energy is being evaporated and used to pump massive amounts of warm air through protected tubes to the upper troposphere, cooling the surface. Above the thunderstorm threshold temperature, little additional radiation energy goes into warming the surface. It goes into evaporation and vertical movement. This means that the climate sensitivity is near zero.

Now it is tempting to argue that the IPCC linearity claim is true because it only applies to a global average temperature. The IPCC only formally say that there is “a linear relationship between global mean RF [radiative forcing] and global mean surface temperature response.” So it might be argued that the relationship is linear for the global average situation.

But the average of non-linear data is almost always non-linear. Since daily forcing and temperature vary non-linearly, there is no reason to think that real-world global averages vary linearly. The real-world global average is an average of days during which climate sensitivity varies with temperature. And the average of such temperature-sensitive records is perforce temperature sensitive itself. No way around it.

The IPCC argument, that temperature is linearly related to forcing, is at the heart of their claims and their models. I have shown elsewhere that in other complex systems, such an assumed linearity of forcing and response does not exist.

Given the centrality of the claim to their results and to the very models themselves, I think that something more than ‘we found linearity in every model we examined” is necessary to substantiate this most important claim of linearity. And given the general lack of linearity in complex natural systems, I would say that their claim of linearity is an extraordinary claim that requires extraordinary evidence.

At a minimum, I think we can say with “high confidence” that it is a claim that requires something more weighty than ‘the models told me so’ ...

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NS
October 25, 2010 6:00 am

I guess there are Newtonian assumptions behind the claim. ie increasing the total energy in the system will cause a linear change in the system where the system is unique. This would assume CO2 is not well-mixed, which is incorrect.

Tom
October 25, 2010 6:05 am

Gosh, that got some backs up…
@Gaylon – I do not dispute the inaccuracy of the GCMs. I dispute the logic employed in this article, specifically the non sequitur summed up as, “Local day-to-day weather does not display linear sensitivity therefore global climate does not display linear sensitivity.”
@Policyguy – Calm down a bit. I am not defending the models – they may be every bit as bad as you say. What I am saying is that Willis doesn’t look into how the models were constructed, or whether an inference of linearity from them is valid – he just removes the citations and assumes the inference is invalid. This is just as much assuming your conclusion as he accuses the IPCC of. Comparing electrons to large-scale current is indeed ridiculous – it was meant to be – just as ridiculous as comparing hour-to-hour tropical weather behaviour with long-term global climate averages. You see the point – why don’t you apply it?
Clague – I’m not sure why this is relevant. My point was not that there is any connection between gas models and how the climate works, and I’m not sure how you’ve managed to read it that way.
And W – I don’t disagree. My point is simply that Willis hasn’t shown either that the IPCC used observations or that they didn’t. He has assumed that the models used are completely disconnected from reality, removed the references to make it harder for you to check, and then concluded that the models are disconnected from reality. He has also shown that local, short-term weather effects are not linear or time invariant and jumped to the conclusion that therefore large-scale, long-term weather effects are not linear or time invariant. He is not necessarily wrong (I think he is probably right), but he hasn’t proven his point, either.

tallbloke
October 25, 2010 6:08 am

From: Tom Wigley
To: Kevin Trenberth
Subject: Re: BBC U-turn on climate
Date: Wed, 14 Oct 2009 16:09:35 -0600
Cc: Michael Mann , Stephen H Schneider , Myles Allen , peter stott , “Philip D. Jones”, Benjamin Santer , Thomas R Karl , Gavin Schmidt , James Hansen , Michael Oppenheimer
Kevin,
I didn’t mean to offend you. But what you said was “we can’t account
for the lack of warming at the moment”. Now you say “we are no where
close to knowing where energy is going”. In my eyes these are two
different things — the second relates to our level of understanding,
and I agree that this is still lacking.
Tom.

Jason Calley
October 25, 2010 6:17 am

Tom Vonk says at Oct 25, 3:47 am, “While this issue is indeed a major issue , one has to be careful with the arguments .
I have to mention that any parameter Y related to a variable X will answer in a linear way (e.g Y=a.X) provided that the variation of X is small .”
I certainly may be misunderstanding things, but I believe that the linear relationship for small variations of x does not hold in chaotic systems. This is not to say that NO values of x have a linear realtionship to y, but only to point out that not ALL values of x have that linear relationship. After all, even chaotic systems have attractors and lslands of stability. It is just that interspersed among those regions of linear relationships we have a fine scattering of chaotic areas. Once x crosses into a chaotic region, we no longer have a linear relationship, and the predictive ability of the model breaks down.
Please correct me if I am mistaken on that.

Martin Lewitt
October 25, 2010 6:18 am

Willis,
In addition to an assumption of linearity, there is an assumption of equivalence between forcing types which is uncritically accepted as show by the mathematical formula for converting W/m^2 to its CO2 doubling equivalent. Even a basic understanding of nonlinear dynamic systems, makes it clear that it is unsafe to assume that forcings with different distributions or different coupling to the climate are equivalent. I found a refreshing acknowledge of this in Hegerl and Knutti’s review article:
“The concept of radiative forcing is of rather limited use for forcings with strongly varying vertical or spatial distributions.”
and this:
“There is a difference in the sensitivity to radiative forcing for different forcing mechanisms, which has been phrased as their ‘efficacy'”
http://www.iac.ethz.ch/people/knuttir/papers/knutti08natgeo.pdf
Unfortunately the “efficacy” is from the work of Hansen, which threw away part of the difference between solar and GHG forcing by using models with simplified oceans and no stratosphere, and coupling GHG to the whole mixing layer of the oceans, when CO2 wavelengths only penetrate microns and solar can penetrate 10s of meters.
On consequence of this assumed equivalence, is that they can try to calculate model independent estimates of climate sensitivity using solar and aerosols and then just translate it to a climate sensitivity to CO2 doubling. Their estimates for solar variability are low, even for the Maunder and Dalton minimums so their sensitivities to solar forcing ends up high and by equivalence CO2 is high. This is why light that the current solar minimum is shedding on solar variability, especially in the UV range. Those “model independent” estimates (which often use models BTW), assume all the solar coupling was purely radiative, while UV couples non-linearly, chemically through the creation of stratospheric and to a lesser extent tropospheric ozone, a greenhouse gas.
The equivalence assumption extends to the models, all they have to do is match the global average temperature and look like a climate. Models with twice the sensitivity and twice the aerosols are equivalent and “match” each other and the climate. So what if the climate produced twice the increase in precipitation (per Wentz), the climate is just one 30 year instance. The models have it out numbered.

October 25, 2010 6:18 am

The rate of energy lost to space with no atmosphere would definitely be non-linear following W=kT^4 where T is the absolute temperature at the surface. With an atmosphere containing clouds and water vapor, T can be for the surface, cloud tops, or water vapor molecules. So what satellites see is a temperature mixture. The rates of evaporation, condensation, freezing, and thawing are factors that tend to control the coefficient k. CO2 has no measurable effect. http://www.kidswincom.net/CO2OLR.pdf

Robinson
October 25, 2010 6:19 am

John Day said:

Climate models have greater uncertainty, so that’s when all the fun begins in these climate blogs, sceptic or otherwise.

Come now. To say there are no “model free” measurements of nature is entirely correct. But if I make a clock from a dandelion and tell you that it measures time – one hour passes for every petal I pick off – you would be justified in questioning the efficacy of my method, notwithstanding the accuracy of my petal picking. Uncertainty isn’t the only problem with climate models, as you well know.

Bill Illis
October 25, 2010 6:24 am

The Stefan Boltzmann equations which are the fundamental equations governing energy and temperature in the universe and on Earth are logarithmic, not linear.
The IPCC top-of-the-atmosphere forcing of 3.7 Watts/m2 has to translate into an extra 16.0 Watts/m2 at the surface to meet the 3.0C per doubling proposition.
Depicted here.
http://img685.imageshack.us/img685/6840/sbearthsurfacetemp.png
http://img213.imageshack.us/img213/2608/sbtempcperwatt.png
The theory starts out by using this formula and then it abandons it half-way through and starts to adopt linearity.
I think they are assuming doubling CO2/GHGs results in an extra +3.7 W/m2 at the tropopause – feedbacks then add another +7.8 W/m2 and the temperature at the tropopause rises +3.0C (using the logarithmic formula) – the IPCC then switches to linearity and assumes the Lapse Rate stays intact at 6.5C/km and the surface warms by the same +3.0C. If they continued to use the logarithmic formula, the surface would warm much less than tropopause.

Martin Lewitt
October 25, 2010 6:26 am

Moderator, apologies for the typos, please post this revision instead:
Willis,
In addition to an assumption of linearity, there is an assumption of equivalence between forcing types which is uncritically accepted as shown by the mathematical formula for converting W/m^2 sensitivity to its CO2 doubling equivalent. Even a basic understanding of nonlinear dynamic systems, makes it clear that it is unsafe to assume that forcings with different distributions or different coupling to the climate are equivalent. I found a refreshing acknowledgement of this in Hegerl and Knutti’s review article:
“The concept of radiative forcing is of rather limited use for forcings with strongly varying vertical or spatial distributions.”
and this:
“There is a difference in the sensitivity to radiative forcing for different forcing mechanisms, which has been phrased as their ‘efficacy’”
http://www.iac.ethz.ch/people/knuttir/papers/knutti08natgeo.pdf
Unfortunately the “efficacy” is from the work of Hansen, which threw away part of the difference between solar and GHG forcing by using models with simplified oceans and no stratosphere, and coupled GHG to the whole mixing layer of the oceans, when CO2 wavelengths only penetrate microns and solar can penetrate 10s of meters.
One, consequence of this assumed equivalence, is that they can try to calculate model independent estimates of climate sensitivity using solar and aerosols and then just translate it to a climate sensitivity to CO2 doubling. Their estimates for solar variability are low, even for the Maunder and Dalton minimums, so their sensitivities to solar forcing ends up high and by equivalence CO2 sensitivity is high. This is why the light that the current solar minimum is shedding on solar variability, especially in the UV range is so important. Those “model independent” estimates (which often use models BTW), assume all the solar coupling was purely radiative, while UV couples non-linearly (chemically) through the creation of stratospheric and to a lesser extent tropospheric ozone, a greenhouse gas.
The equivalence assumption extends to the models, all models have to do is match the global average temperature and look like a climate. Models with twice the sensitivity and twice the aerosols are equivalent and “match” each other and “match” the climate. So what if the climate produced twice the increase in precipitation (per Wentz), the climate is just one 30 year instance. The models have it out numbered.

DirkH
October 25, 2010 6:26 am

“I gotta confess, that wasn’t the first time I’ve walked away from the IPCC Report shaking my head[…]”
Oh come on Willis. They’re kids. They’ll grow up and learn a thing, maybe.
http://nofrakkingconsensus.wordpress.com/2010/10/22/an-even-younger-senior-author/

Francisco
October 25, 2010 6:34 am

Tom says:
“the randomness of small-scale weather processes doesn’t disprove the overall theory of linearity any more than observing individual electron tunnelling in silicon disproves Ohm’s law.”
==========
Ohm’s law holds only for so called ohmic materials (metals) that exhibit roughly constant resistance at roughly constant temperature, and even then it is only a good approximation.
Most materials in the real world thumb their nose entirely at Ohm’s law by showing all kinds of non-constant (i.e. voltage-dependent) resistance/conductance.
I’ve heard biologists aptly refer to Ohm’s law as “Ohm’s dream”.

simpleseekeraftertruth
October 25, 2010 6:44 am

It could be that the straight line is only a tangent: but they would have thought of that wouldn’t they?
🙂

Neil Jones
October 25, 2010 6:48 am

WYSIWYG Science – What You Seek Is What You Get.

October 25, 2010 6:59 am

The IPCC wrote “…excluding cloud-aerosol interaction effects…”
I read “… ignoring the dog we know where this tail is going…”

Malaga View
October 25, 2010 7:00 am

Forcing is generally taken to mean downward radiation measured at the TOA (top of atmosphere). The IPCC says that when TOA forcing changes, the surface temperature changes linearly with that TOA forcing change.

Willis: Thanks for another illuminating posting… slowly, slowly the seven veils are being stripped away… only to reveal an IPCC emperor with no clothes…
The thing that makes me smile about the concept of climate sensitivity is that it means the SUN must have been responsible for driving the surface temperature up during the Medieval Warm Period and down during the Little Ice Age.
The Intergovernmental Panel of Climate Clairvoyants just make up the rules as they go along… nothing hangs together… circular logic… conflicting rules… assumptions… fudged data… bogus models… propaganda… evasion… conspiracy… deception… misdirection… and lies… my oh my… what wonders they can see in their crystal balls…. Climate Science has become an oxymoron.
The typical tropical day clearly illustrates the non-linear climate sensitivity for a specific spot on the globe.. and lets hope there is not a cold northerly wind or a hot southerly wind that will affect their observations… and when they get to the night they see temperatures fall over time… surely they know the basic science behind the non-linear Cooling Curve http://en.wikipedia.org/wiki/Cooling_curve
Even if they you managed to establish the climate sensitivity for a specific spot on the globe it would still vary up the air column (say up to tree height or beyond) and by season for that spot on the globe… and how about the local area… it can be different 100 meters away, let alone 100 kilometers… and trying to say that somehow they have established a global climate sensitivity is simply beyond comprehension… let alone that it is linear… so now they have morphed into the Intergovernmental Panel of Climate Crazies.

Bruckner8
October 25, 2010 7:01 am

I think the response here are missing the point entirely. (Understandable, considering the effort Willis went through to show why he thinks these ideas are non-linear.)
To me, the important takeaway is: THEY DID NOT USE ANY REAL OBSERVATIONS.
Once I realized that, their entire point of using models was completely futile. Models are meant to be tested against reality to increase their validity. Therefore, a model is practically ASSUMED to be programmed using reality-based observations as its #1 tenet. Willis showed this to assumption to be false.
I couldn’t care less whether this or that forcing is PROGRAMMED to be linear or not…but I am deeply concerned that their processes led them to think it was OK to make such a program without a correct scientific process: starting with observation and tested against observation!

Doug in Seattle
October 25, 2010 7:02 am

Willis, I applaud you on finding this particular pea in the IPCC shell game. The next step is to find an alternative explanation that better describes climate sensitivity.
I think Tom’s argument above regarding averaging of complex systems has some validity. In hydrogeology we use similar empirical rules to characterize and calculate groundwater flow when we know that at smaller scales groundwater acts quite differently.
I am not a big fan of invoking chaos as an explanation. I suspect that chaos is too often used as an explanation when something is complex and therefore too troublesome to figure out. Natural phenomena appear to me to exhibit a form of symmetry at all scales. This seems to argue against chaos.

Alex the skeptic
October 25, 2010 7:14 am

Two centuries ago, the river Thames used to freeze over. Then it didn’t any more. What went wrong? Was it linearity? These last winters England suffered cold snaps such as: http://news.bbc.co.uk/2/hi/in_depth/8447023.stm
When the UK mainland was 100% covered in snow. Also, the northern hemisphere had the largest land snow cover ever recorded. Is this linearity. Of course not. It’s called climate change. The climate change deniers are not us but them; those who are playing around with multimillion dollar computer games funded by our taxes. The IPCC are the climate deniers because its them would deny that climate is continuously changing.
Now the AGWers want to control it by reducing CO2. They want to software-design our climate but Anthropogenic Climate Design will not work.

Alex the skeptic
October 25, 2010 7:18 am

Two centuries ago, the river Thames used to freeze over. Then it didn’t any more. What went wrong? Was it linearity? Then the warmth came, then these last winters England suffered cold snaps such as>: http://news.bbc.co.uk/2/hi/in_depth/8447023.stm < when the UK mainland was 100% covered in snow. Also, this 2009/10 winter the northern hemisphere had the largest land snow cover ever recorded. Is this linearity. Of course not. It's called climate change. The climate change deniers are not us but them; those who are playing around with multimillion dollar computer games funded by our taxes. The IPCC are the climate deniers because its them that deny that climate is continuously changing. They say it has not changed during the past except now due to my breathing out CO2.
Now the AGWers want to control the climate by reducing/adding CO2 . They want to software-design our climate but Anthropogenic Climate Design will never work.
Great post Willis.

Kevin_S
October 25, 2010 7:25 am

I love it. This paper would be akin to me building a model of a tugboat and then calling it the U.S.S. Missouri and when someone points out that it isn’t, well I have a model saying it is therefore there is a problem with the actual real world observations.

Rhys Jaggar
October 25, 2010 7:27 am

Tell the IPCC to go look again at Hooke’s Law.
What you’ll see is that there are CERTAIN CONDITIONS WHERE LINEARITY IS APPROPRIATE, but equally, OTHERS WHERE IT IS NOT.
This is the reality of nature.
As is the possibility, ubiquitous in biology, that the same forcing agent can elicit completely different types of responses depending on concentration.
As an example ‘tumour necrosis factor alpha’, named thus for its ability to cause tumours to die and necrose (become dead tissue) is actually a stimulant of capillary growth at much lower concentrations than those which cause tumour death.
Similarly, the effect of one forcing agent AT PARTICULAR CONCENTRATIONS OF OTHER FORCING AGENTS may be different at the same concentration IF OTHER FORCING AGENTS’ CONCENTRATIONS ARE SIGNIFICANTLY DIFFERENT.
I think the mathematicians call it ‘partial differential equations’…….

RC Saumarez
October 25, 2010 7:28 am

I agree with Tom Vonk. The processes may be non-linear but for small variations the relationship between variables may be linearisable as in the Galerkin method. This is process that pervades engineering mathematics and is fine as long as you understand what is meant by a small variation in terms of non-linearity.
If forcings change by 2%, one may not be able to distinguish non-linearity; they change by 30%, 50% and 100%, deviation from linearity may become apparant. Another property of linear systems is superposition so that if the temperature responds to the sum of forcings as the sum of the individual forcings.
Given that the overall effects in climate may be due to rellatively small changes in forcings and that non-linearities, such as those described in this post, are local, linearity may no be a bad assumption given lack of experimental data demonstrating overall non-linear behaviour.

October 25, 2010 7:28 am

Oh dear. All differentiable functions are linear to first order in small quantities. It doesn’t matter how convoluted and nonlinear, or even chaotic, the curve might be, or how strong the feedbacks, it is still linear for small changes. What we are talking about here are small changes in the energy balance, of a few parts per thousand (~1W/m2 in ~1kW/m2) . So the predictable or average component of the response of the global temperature to such changes cannot help but be close to linear. The only way that could fail is if the response is pathological – either about to run into the buffers or to fall off a cliff in a (mathematical, if not physical) catastrophe – which only the most absurdly alarmist would claim.
By the way, the term “forcing” is wrong; a forcing is an oscillation imposed upon a system other than at its resonant frequency.

October 25, 2010 7:35 am

Under certain conditions, the “global mean response” might scale approximately linearly with the “global mean forcing” but clearly this assumption cannot possibly hold under all conditions. Imagine, hypothetically, a “Snowball Earth” which is completely covered in ice. Obviously one can make it colder by reducing solar luminosity or something. But that cooling cannot be greatly augmented by ice albedo feedback, as there is nowhere for additional ice to form to reflect more sunlight. But, if we warm up the Earth a little bit when it is extremely cold, the ice melting at the equator would create a strong positive feedback. Likewise, a totally ice free Earth would not have an ice albedo feedback toward warming, but it would toward cooling. So that feedback is not only dependent on the initial conditions, but also on the direction of temperature change. An even more interesting case, of water vapor in extreme cold conditions (indeed, at sufficiently cold temperatures, all atmosphere GHG’s) is that if the Earth is cooled down by a very large amount, these gases would have to condense out of the atmosphere, thus eliminating their warming effect and acting as a positive feedback toward more cooling. However, while the temperature does limit the amount of water vapor the atmosphere can hold, a warmer atmosphere does not HAVE to hold more water vapor-while a much colder one must lose water vapor. So this feedback must be strongly positive for very cold temperatures, but it need not be a warmer ones. The examples I can think of, much like Willis’s suggest that the “sensitivity” will tend to be higher at lower temperatures. However, my examples would make large differences mainly in extreme climate states, and for them the linearity assumption may be okay at conditions similar to the present. But Willis’s example is intriguing because it is not merely of hypothetical curiosity, it may make a big difference in the temperature range associated with the present, real case.

Steve Keohane
October 25, 2010 7:37 am

LazyTeenager says: October 25, 2010 at 5:09 am
PolicyGuy says
——————-
nothing but models that perform as they were written to perform
——————-
You know nothing about the models and you are pretending insight where you have none. You have been fooled by your own sophistry.

Pot meet kettle, don’t know much about computers do you. I assumed those who grew up with them might understand the computers’ limitations, guess I was wrong. Or maybe the teenager part of your moniker is a fib.